library(tidyverse)
library(DT)
library(lubridate)
Load the data into R.
Descriptive Analysis
2-1.
2-2.
2-3.
and
more..
Determine the optimal number of clusters using methods like the
Elbow method.
Perform K-means clustering.
Analyze the resulting clusters to interpret different groupings
of orders based on acknowledgment times and other relevant
factors.
Load the data into R.
I’ve loaded already. Let’s take a look at the dataset to get a sense
of what we’re working with.
order_late %>%
dplyr::mutate(order_date = as.Date(order_date),
delivery_date = as.Date(delivery_date),
ship_date = as.Date(ship_date),
date_acknowledge = as.Date(date_acknowledge),
date_acknowledgement_calc = as.Date(date_acknowledgement_calc)) %>%
DT::datatable(options = list(scrollX = TRUE))
Data Description:
- profile_owner: The identifier of the individual who owns the profile
related to the order.
- profile_name: The full name of the profile owner associated with the
order.
- enter_by: The identifier of the individual who entered the order
into the system.
- enter_by_name: The full name of the individual who entered the order
into the system.
- leader: The identifier of the leadership or supervisory figure
associated with the order or the profile owner.
- leader_name: The full name of the leader or supervisor associated
with the order.
- loc: A code or number that represents the location where the order
was processed or is to be fulfilled from.
- order: The unique identifier assigned to the order.
- customer_name: The name of the individual or entity to whom the
order will be delivered.
- customer: A unique identifier ssociated with the customer.
- order_date: The date on which the order was placed or recorded.
- week_number: The week of the year when the order was placed, which
could be useful for seasonal analysis.
- delivery_date: The date when the order is scheduled to be delivered
to the customer.
- ship_date: The actual date when the order was shipped out from the
facility.
- date_acknowledge: The date on which the order acknowledgment was
recorded in the system.
- date_acknowledgement_calc: Calculated date for when the order was
supposed to be acknowledged, possibly used for performance
tracking.
- days_to_acknowledge: The number of days it took to acknowledge the
order from the order date, a measure of processing time.
- On Time: An indicator of whether the order acknowledgment was within
the expected time frame, with values like ‘On Time’ or ‘Not on
Time’.
These columns together can provide valuable insights into the order
processing efficiency and timeliness. Understanding patterns and
relationships within these columns through clustering or other data
analysis methods could help in identifying bottlenecks, predicting
future performance, and improving overall service delivery.
Next: Descriptive Analysis